This invention relates generally to the development of models to optimize the effects of targeted marketing programs. More specifically, this invention maximizes modeling results within a specific working interval, so that lift within the working interval is higher than that obtained using traditional modeling methods.
The goal of targeted marketing modeling is typically to find a method to sort a set of prospective customers based on their attributes in a such a way that the cumulative response rate lift, or other desired or undesired behavior, for a given interval of the customer set (say the top 20%, the bottom 20%, or the middle 30%) is as high as possible, and the separation has a high level of significance (i.e., it offers significant predictive power).
The traditional approach to this problem is as follows: First, a model is built to simulate the probability of response as a function of attributes. Model parameters are computed in a special model fitting procedure. In this procedure the output of the model is tested against actual output and discrepancy is accumulated in a special error function. Different types of error functions can be used (e.g., mean square, absolute error); model parameters should be determined to minimize the error function. The best fitting of model parameters is an xe2x80x9cimplicitxe2x80x9d indication that the model is good, but not necessarily the best, in terms of its original objective.
Thus the model building process is defined by two entities: the type of model and the error or utility function. The type of model defines the ability of the model to discern various patterns in the data. For example, Neural Network models use more complicated formulae than Logistic Regression models, thus Neural Network models can more accurately discern complicated patterns.
The xe2x80x9cgoodnessxe2x80x9d of the model is ultimately defined by the choice of an error function, since it is the error function that is minimized during the model training process.
Prior art modeling processes share two common drawbacks. They all fail to use the response rate at the top of the sorted list as a utility function. Instead, Least Mean Square Error, Maximum Likelihood, Cross-Entropy and other utility functions are used only because there is a mathematical apparatus developed for these utility functions. Additionally, prior art processes assign equal weight to all records of data in the sorted list. The marketers, however, are only interested in the performance of the model in the top of the list above the cut-off level, since the offer will be made only to this segment of customers. Prior art methods decrease the performance in the top of the list in order to keep the performance in the middle and the bottom of the list on a relatively high level.
What is needed is a process that builds a response model directly maximizing the response rate in the top of the list, and at the same time allows marketers to specify the segment of the customer list they are most interested in.
The present invention comprises a method that overcomes the limitations of the prior art through an approach which is best used to maximize results within a specific working interval to outperform industry standard models in the data mining industry. Standard industry implementations of neural network, logistic regression, or radial basis function use the technique of Least Means Squared as the method of optimizing predictive value. While correlated with lift, Least Means Squared acts as a surrogate for predicting lift, but does not explicitly solve for lift.
The present invention explicitly solves for lift, and therefore accomplishes the goal of targeted marketing. Mathematically, the effectiveness of a model that is based on the present invention is greater than models based on conventional prior art techniques.
The present invention explicitly solves for lift by:
Sorting customer/prospect list by predicted output variable outcome;
Calculating the integral criterion defined as a measure of lift over the desired range by using the known responders and non-responders;
Iterating on set of input parameters until overfitting occurs, (i.e., the utility function of the testing set begins to diverge from utility function of the testing set); and
Testing of these results are then performed against the validation set.
There are other advantages to using the present invention over existing commercial techniques. First, it can be tuned to a predefined interval of a sorted customer list, for example from 20% to 50%. By ignoring the sorting outside the interval, the integral criterion of lift inside the working interval is higher.
Second, it is model independent. It may be used with a variety of different modeling approaches: Neural Network, Logistic Regression, Radial Basis Function, CHAID, Genetic Algorithms, etc.
The superior predictive modeling capability provided by using the present invention means that marketing analysts will be better able to: (i) predict the propensity of individual prospects to respond to an offer, thus enabling marketers to better identify target markets; (ii) identify customers and prospects who are most likely to default on loans, so that remedial action can be taken, or so that those prospects can be excluded from certain offers; (iii) identify customers or prospects who are most likely to prepay loans, so a better estimate can be made of revenues; (iv) identify customers who are most amenable to cross-sell and up-sell opportunities; (v) predict claims experience, so that insurers can better establish risk and set premiums appropriately; and identify instances of credit-card fraud.